research Projects

Our research covers the transmission of sound with hearing devices. This process starts with the sensory input, passing through speech processors and neural stimulation before being perceived by the listener and interpreted as speech. Our goal is to understand and improve this process by using technological innovations. This includes computational models, signal processing and deep neural networks as well as electrophysiological and perceptual experiments.

Our recent review on
Cochlear implant research in the 21st century can be found here:

And our introductory article on Smart Hearing Devices in Frontiers for Young Minds here:

Sensory input

Speech processors

Neural stimulation

Perception & Cognition

sensory input

This research investigates different input modalities of sound transmission (acoustic, tactile, electric) and their combinations. We use acoustic simulations of sound perception with cochlear implants and assess whether integration of tactile and auditory input helps CI listening. We also investigate whether hearable devices live up to their claims of helping people with hearing loss.


This multi-centre project led by Dr Mark Fletcher at the University of Southampton investigates the combination of tactile and auditory stimulation to enhance speech perception with cochlear implants. A prototype device is being developed.


This collaboration with Dr Saima Rajasingam at Anglia Ruskin University is a series of investigations into listening performance with and attitudes towards Hearables for people with mild-to-moderate hearing loss.

Related publications

  • Fletcher, M., Hadeedi, A., Goehring, T., Mills, S. (2019). Electro-haptic hearing: speech-in-noise performance in cochlear implant users is enhanced by tactile stimulation of the wrists. Scientific Reports, 9(1), 1-8.

  • Fletcher, M., Mills, S., Goehring, T. (2018). Vibro-tactile enhancement of speech intelligibility in multi-talker noise for simulated cochlear implant listening. Trends in Hearing, 22.

speech processing

We focus on improving speech signals before they are presented to the listener (pre-processing), for example by removing background noise or other acoustic interferences. We use powerful methods from deep learning (deep neural networks) and digital signal processing (adaptive filters) to facilitate speech perception with hearing devices.

Speech enhancement based on DEEP neural networks

We develop noise-reduction algorithms based on deep neural networks (DNNs), to enhance speech perception in noisy situations. The DNNs are optimised with many thousand examples of noisy speech and then evaluated in listening studies with cochlear implant and hearing aid users.

Ongoing work includes:

  • Evaluation of multi-microphone algorithms (Led by Dr ClĂ©ment Gaultier)

  • Development of speaker-aware algorithms (Led by Iordanis Thoidis)

  • Optimisation for cochlear implant speech processing


In this collaboration with Dr Alan Archer-Boyd and Dr Charlotte Garcia we developed an adaptive algorithm to filter-out interfering background sounds in realistic situations (e.g. in a coffeeshop).
In practice, obtaining a reference signal via streaming could facilitate speech perception.

Related publications

  • Goehring, T., Keshavarzi, M., Carlyon, R., Moore, B. (2019). Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants. The Journal of the Acoustical Society of America, 146(1), 705-718.

  • Keshavarzi, M., Goehring, T., Turner, R., Moore, B. (2019). Comparison of effects on subjective intelligibility and quality of speech in babble for two algorithms: a deep recurrent Neural network and spectral subtraction. The Journal of the Acoustical Society of America, 145(3), 1493-1503.

  • Keshavarzi, M., Goehring, T., Zakis, Z., Turner, R., Moore, B. (2018). Use of a deep recurrent neural network to reduce wind noise: effects on judged speech intelligibility and sound quality. Trends in Hearing, 22.

  • Monaghan, J., Goehring, T., Yang, X., Bolner, F., Wang, S., Bleeck, S. (2017). Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners. The Journal of the Acoustical Society of America, 141(3), 1985-1998.

  • Goehring, T., Bolner, F., Monaghan, J., Van Dijk, B., Zarowski, A., Bleeck, S. (2017). Speech Enhancement Based on Neural Networks Improves Speech Intelligibility in Noise for Cochlear Implant Users. Hearing Research, 344, 183-194.

  • Goehring, T., Yang, X., Monaghan, J., Bleeck, S. (2016). Speech enhancement for hearing-impaired listeners using deep neural networks with auditory-model based features. In 2016 EURASIP 24th European Signal Processing Conference (EUSIPCO) (pp. 2300-2304), Hungary. IEEE.

  • Bolner, F., Goehring, T., Monaghan, J., Van Dijk, B., Wouters, J., Bleeck, S. (2016). Speech enhancement based on neural networks applied to cochlear implant coding strategies. In 2016 IEEE Intern. Conf. on Acoustics, Speech and Signal Processing (ICASSP), China. IEEE.

Neural Stimulation

These projects investigate the electro-neural interface and stimulation patterns with cochlear implants and their impact on speech perception. We develop new coding strategies, assess channel interaction effects and build computational models for the electrical stimulation and sound transmission with cochlear implants.

Speech coding strategies for cochlear implants

We develop novel speech coding strategies to improve speech perception with cochlear implants:

  • TIPS: Temporal integrator processing strategy (Project led by Dr Lidea Shahidi)

A novel strategy for cochlear implants to improve speech perception in noise and to reduce power consumption. We are improving the robustness to various acoustic scenarios and are developing a real-time implementation.

Spectral blurring in cochlear implants

In this project with Dr Bob Carlyon (CBU) and Prof Julie Arenberg (Harvard) we manipulate channel interaction with cochlear implants to assess its effects on speech perception. Our findings guide future development of speech processing strategies and clinical assessment.


Project led by Dr Tim Brochier with Dr Josef Schlittenlacher, Dr Iwan Roberts, Dr Chen Jiang, Dr Debi Vickers and Prof Manohar Bance.

We have built high-resolution computational models in combination with automatic speech recognition to assess information transmission with cochlear implants.

patient-specific cochlear implant stimulation PATTERNS

Project led by Dr Charlotte Garcia to develop a model-based algorithm (PECAP) for estimating patient-specific excitation profiles to characterise stimulation and neural health patterns.

  • The PECAP algorithm is currently used by several clinics and follow-on projects

  • The PECAP algorithm has recently been made faster ("SpeedCAP", Garcia et al. 2022)

  • The PECAP algorithm successfully detected blurred stimulation and neural dead regions

Related publications

  • Garcia, C., Deeks, JM., Goehring, T., Borsetto, D., Bance, M., Carlyon, RP. (2022). SpeedCAP: An Efficient Method for Estimating Neural Activation Patterns Using Electrically-Evoked Compound Action-Potentials in Cochlear Implant Users. Ear and Hearing.

  • Brochier, T., Schlittenlacher, J., Roberts, I., Goehring, T., Jiang, C., Vickers, D., Bance, M. (2022). From Microphone to Phoneme: An End-to-End Computational Neural Model for Predicting Speech Perception with Cochlear Implants. IEEE Transactions on Biomedical Engineering.

  • Garcia, C., Goehring, T., Cosentino, S., Turner, R. E., Deeks, J. M., Brochier, T., ... & Carlyon, R. P. (2021). The panoramic ECAP method: estimating patient-specific patterns of current spread and neural health in cochlear implant users. Journal of the Association for Research in Otolaryngology, 1-23.

  • Jiang, C., Singhal, S., Landry, T., Roberts, I., De Rijk, S., Brochier, T., Goehring, T., ... & Malliaras, G. G. (2021). An Instrumented Cochlea Model for the Evaluation of Cochlear Implant Electrical Stimulus Spread. IEEE Transactions on Biomedical Engineering.

  • Goehring, T., Archer-Boyd, A., Arenberg, J., Carlyon, RP. (2021). The effect of increased channel interaction on speech perception with cochlear implants. Scientific Reports.

  • Goehring, T., Arenberg, J., Carlyon, R. (2020). Using spectral blurring to assess effects of channel interaction on speech-in-noise perception with cochlear implants. Journal of the Association for Research in Otolaryngology.

  • Lamping, W., Goehring, T., Marozeau, J., Carlyon, R. (2020). The effect of a coding strategy that removes temporally masked pulses on speech perception by cochlear implant users. Hearing Research, 391, 107969.

  • Goehring, T., Archer-Boyd, A., Deeks, J., Arenberg, J., Carlyon, R. (2019). A site-selection strategy based on polarity sensitivity for cochlear implants: effects on spectro-temporal resolution and speech perception. Journal of the Association for Research in Otolaryngology, 1-18.

Perception & cognition

We assess different aspects of speech perception. This includes signal qualities, such as spectral and temporal resolution, and speech perception in terms of intelligibility, quality and listening effort as well as through electrophysiological EEG measures.

spectro-temporal resolution

Project led by Dr Alan Archer-Boyd and Dr Bob Carlyon.

Investigations of spectro-temporal resolution with cochlear implants using the STRIPES test.

  • We published a new online version of the STRIPES test (Archer-Boyd et al. 2022)

Speech perception: Listening experiments

We use a range of measures for assessing speech perception, such as measuring speech intelligibility, speech quality and tolerance thresholds for distortions and artefacts.

  • Our new online test system, AUDITO, is currently being finalised and will be used to perform online listening experiments with cochlear implant users.

Speech transmission index: Neural entrainment

In this project led by Dr Alexis Deighton MacIntyre we use electrophysiological markers via EEG measurements to assess speech entrainment and develop objective indices of perception.

  • We developed a novel listening paradigm to overcome limitations of previous approaches.

Related publications

  • Archer-Boyd A., Harland, A., Goehring T., Carlyon, RP. (2022). An online implementation of a measure of spectro-temporal processing by cochlear-implant listeners. JASA-EL.

  • Archer-Boyd, A., Goehring, T., Carlyon, R. (2020). The effect of free-field presentation and processing strategy on a measure of spectro-temporal processing by cochlear-implant listeners. Trends in Hearing,

  • Goehring, T., Chapman, J., Bleeck, S., Monaghan, J. (2018). Tolerable delay for speech production and perception: effects of hearing ability and experience with hearing aids. International Journal of Audiology, 57(1), 61-68.